### `set_linter` only
* Fix gnarly [bug](dbed747aae/tools/test/set_linter_testdata/python_code.py.txt.python (L42)) which would have garbled Python files involving sets contained in sets.
* Better handling of new Python3.12 token types
### Both linters.
* Recover from and report on unparseable Python files
* Remove `ParseError.check()` (it made it harder to read the code)
* FileLinter is now generic on `PythonFile`
### Notes
As I started working on new docstring features, I found a nasty bug and an edge case bug in set linter, and realized both the linters crash when there is a badly-formed Python file in the repo.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144620
Approved by: https://github.com/amjames, https://github.com/jansel
Manually map test_cpp_extensions_aot_ninja to files in test/cpp_extensions since test_cpp_extensions_aot_ninja isn't an actual file you can edit, but a wrapper for files in test/cpp_extensions.
Idk if this is a good idea, feels very manual. Maybe it would be better to classify this the same as any other TD failure where TD simply can't figure out the tests it needs to run
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148992
Approved by: https://github.com/malfet, https://github.com/seemethere, https://github.com/janeyx99
This PR adds two main parts:
- shim.h stable C APIs into torch::Library APIs
- a higher level API in torch/csrc/stable/library.h that calls into this shim.h + otherwise is self contained
Goal: custom kernel writers should be able to call the apis in the directories above in order to register their library in a way that allows their custom extension to run with a different libtorch version than it was built with.
Subplots resolved:
- Do we want a whole separate StableLibrary or do we want to freeze torch::Library and add `m.stable_impl(cstring, void (*fn)(void **, int64_t, int64_t)` into it
- Yes, we want a separate StableLibrary. We cannot freeze Library and it is NOT header only.
- Should I use unint64_t as the common denominator instead of void* to support 32bit architectures better?
- Yes, and done
- Should I add a stable `def` and `fragment` when those can be done in python?
- I think we do want these --- and now they're done
- Where should library_stable_impl.cpp live? -- no longer relevant
- I need some solid test cases to make sure everything's going ok. I've intentionally thrown in a bunch of random dtypes into the signature, but I still haven't tested returning multiple things, returning nothing, complex dtypes, etc.
- Have since tested all the torch library endpoints. the others can be tested in a followup to separate components that need to be in shim.h vs can be added later
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148124
Approved by: https://github.com/albanD, https://github.com/zou3519, https://github.com/atalman
This allows for each device type to check current devices for Triton compatibility and ensure their Triton backend is present.
This PR replaces the `has_triton()` global method which was previously used for this task, and moves the initial check for each Inductor backend on to their associated `BaseScheduler` subclass. This means that other backends, such as Halide, can also implement their own availability checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139171
Approved by: https://github.com/jansel
Because Clang-tidy 19 has more powerful clang-analyzer checks to detect subtle bugs. New checks such as misc-use-internal-linkage can help identify potential static variables or functions, thus reducing binary sizes.
Some new checks are disabled temporarily for later enabling. Additional warnings have been fixed or suppressed.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148648
Approved by: https://github.com/Skylion007
This PR adds two main parts:
- shim.h stable C APIs into torch::Library APIs
- a higher level API in torch/csrc/stable/library.h that calls into this shim.h + otherwise is self contained
Goal: custom kernel writers should be able to call the apis in the directories above in order to register their library in a way that allows their custom extension to run with a different libtorch version than it was built with.
Subplots resolved:
- Do we want a whole separate StableLibrary or do we want to freeze torch::Library and add `m.stable_impl(cstring, void (*fn)(void **, int64_t, int64_t)` into it
- Yes, we want a separate StableLibrary. We cannot freeze Library and it is NOT header only.
- Should I use unint64_t as the common denominator instead of void* to support 32bit architectures better?
- Yes, and done
- Should I add a stable `def` and `fragment` when those can be done in python?
- I think we do want these --- and now they're done
- Where should library_stable_impl.cpp live? -- no longer relevant
- I need some solid test cases to make sure everything's going ok. I've intentionally thrown in a bunch of random dtypes into the signature, but I still haven't tested returning multiple things, returning nothing, complex dtypes, etc.
- Have since tested all the torch library endpoints. the others can be tested in a followup to separate components that need to be in shim.h vs can be added later
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148124
Approved by: https://github.com/albanD, https://github.com/zou3519
Fixes https://github.com/pytorch/executorch/issues/8711
In ExecuTorch when we try to parse the following schema:
```
aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor
```
Repro:
```python
from torchgen.model import FunctionSchema
native_schema = FunctionSchema.parse("aten::__lshift__.Scalar(Tensor self, Scalar other) -> Tensor")
```
It's failing because `BaseOperatorName` categorizes it to be a
inplace operator.
I understand we are not supposed to pass in namespace "aten::" into
`FunctionSchema.parse()` but unfortunately ExecuTorch requires this
feature to work.
This PR adds a new `namespace` attribute to `BaseOperatorName` and makes
sure the rest of the stack works as before, if a schema without
namespace is passed in
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148038
Approved by: https://github.com/bdhirsh
Some disabled test runs weren't being uploaded as disabled tests because some dynamo tests are set to mark themselves as skipped if they are failing. This makes the script think that there are fewer retries than there are actually are and that the job is not a rerun disabled tests job. Instead, query for the job name to see if it contains rerun disabled tests and fall back to counting the number of retries if querying fails
Alternate options: relax the check for the number of tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148027
Approved by: https://github.com/huydhn
Reference: https://docs.astral.sh/ruff/formatter/black/#assert-statements
> Unlike Black, Ruff prefers breaking the message over breaking the assertion, similar to how both Ruff and Black prefer breaking the assignment value over breaking the assignment target:
>
> ```python
> # Input
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
>
> # Black
> assert (
> len(policy_types) >= priority + num_duplicates
> ), f"This tests needs at least {priority+num_duplicates} many types."
>
> # Ruff
> assert len(policy_types) >= priority + num_duplicates, (
> f"This tests needs at least {priority + num_duplicates} many types."
> )
> ```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144546
Approved by: https://github.com/malfet
Following triton # 4916, the generated cubin expects a global_scratch argument to support on-device TMA. We believe this is the source of many of the "invalid argument" failures on AOTI/cpp_wrapper tests. AFAIK, we don't use on-device TMA in Inductor as of now, so it should be safe to use a nullptr for the scratch space.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148051
Approved by: https://github.com/YUNQIUGUO
Summary:
# Summary
### Sticky points
Cuda-graph rng handling has changed / deviated from original implementation. We will be left with a dangling 'offset' val and confusing naming due to BC
## Dependencies
- Flash PR: https://github.com/Dao-AILab/flash-attention/pull/1419
### Other Points
- The BC linter is complaining about losing generate.py and its functions which is not real BC surface
cc albanD
imported-using-ghimport
Test Plan:
Imported from OSS
Building in dev
`buck build @//mode/dev-nosan -c fbcode.nvcc_arch=h100a //caffe2:ATen-cu --show-full-output `
I and Nming the .so I do see that the flash symbols are correctly named:
```
0000000001c3dfb0 t pytorch_flash::run_mha_bwd(pytorch_flash::Flash_bwd_params&, CUstream_st*)::$_0::operator()() const::{lambda()#1}::operator()() const::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const
0000000001c36080 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()#2}::operator()() const::{lambda()#1}::operator()() const::{lambda()#6}::operator()() const
0000000001c360e0 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()#2}::operator()() const::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const
0000000001c35fc0 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()#1}::operator()() const::{lambda()#1}::operator()() const::{lambda()#6}::operator()() const
0000000001c36020 t pytorch_flash::run_mha_fwd(pytorch_flash::Flash_fwd_params&, CUstream_st*, bool)::$_0::operator()() const::{lambda()#1}::operator()() const::{lambda()#1}::operator()() const::{lambda()#7}::operator()() const
```
Reviewed By: vkuzo
Differential Revision: D68502879
Pulled By: drisspg
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146372
Approved by: https://github.com/jbschlosser
Summary:
Continuing the work from https://github.com/pytorch/pytorch/pull/146427
Adds the `torch.float8_e8m0fnu` dtype to PyTorch, as detailed in
https://github.com/pytorch/pytorch/issues/146414 . Please see the issue for a detailed definition of the format. Example of basic functionality:
```python
import torch
# round trip
x0 = torch.randn(4, 4, dtype=torch.float32)
x1 = x0.to(torch.float8_e8m0fnu) # RNE rounding
x2 = x1.to(torch.float32) # 2 ** exponent
# creation with empty
x0 = torch.empty(4, 4, dtype=torch.float8_e8m0fnu)
# printing
print(x0)
```
Done in this PR:
* numerical correctness
* op coverage (except for `torch._scaled_mm`): create tensor, cast to/from float32
* printing a tensor works
For future PRs:
* performance optimizations for casting
* torch._scaled_mm
* PT2
* various cleanups (detailed in comments with issue numbers)
Test Plan:
```
pytest test/quantization/core/experimental/test_float8.py -s
```
Reviewers:
Subscribers:
Tasks:
Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147466
Approved by: https://github.com/drisspg
The original MatchState type was declared as a python Enum. Although we did make it callable but we consume it right away. There are downstream cases when we need it to be a python class which is not supported in Python enum. So we did a small refactoring so that we keep both the enum state and dynamic info (culprit) for the fr analysis script.
Differential Revision: [D69830994](https://our.internmc.facebook.com/intern/diff/D69830994)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147439
Approved by: https://github.com/fegin
By appending `-frecord-sources -gline-tables-only` to the compilation command
Helpful when debugging shaders compiled into libtorch
Test plan: Run
`python ../tools/build_with_debinfo.py ../aten/src/ATen/native/mps/kernels/UpSample.metal ../aten/src/ATen/native/mps/operations/UpSample.mm`
And then run following to capture shader and check that it contains debug info
```python
import torch
import os
os.environ["MTL_CAPTURE_ENABLED"]="1"
inp = torch.rand(size=(6, 3, 10, 20), device="mps", dtype=torch.float32)
with torch.mps.profiler.metal_capture("bilinear2d"):
out = torch.nn.functional.interpolate(x, scale_factor=(1.7,0.9), mode="bilinear")
```
<img width="769" alt="image" src="https://github.com/user-attachments/assets/e0316c1c-07a4-4da5-97b9-886c56857c1d" />
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146768
Approved by: https://github.com/dcci
If the command is too long, the linter fails with
```
Failed due to OSError:
[Errno 7] Argument list too long: 'grep'
```
Fix this by batching the command so it is shorter
Limit of 750k was chosen due to `getconf ARG_MAX` returns ~1M on my mac. My guess is that most people shouldn't hit this unless they run --all-files and the directory length is long.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145950
Approved by: https://github.com/wdvr
This is an attempt to fix flaky mypy errors in CI that look like:
```
dmypy status --verbose
connection_name : /var/folders/rf/qrn1jkgj0b9_tcznwp8ck46w0000gn/T/tmpjoqsid7_/dmypy.sock
pid : 32233
error : timed out
Daemon is stuck; consider /Users/zainr/pytorch/venv/bin/dmypy kill
```
"Fix" it by not using the daemon at all, since it doesn't actually provide any perf benefits in CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145961
Approved by: https://github.com/malfet
